Material and methods
The study was carried out during the years 2016 and 2017 in an
even-aged, pure beech stand (Fagus sylvatica L.) located at Selva
Piana (Collelongo, Abruzzi Region, Italy 41°50’58” N, 13°35’17” E,
1560 m elevation) included in a 3000 ha forest within the external belt
of Abruzzo-Lazio-Molise National Park (Central Apennine). The last
dendrometric survey (2017) assessed a stand density of 725 trees
ha−1, a basal area of 45.77 m2ha−1, a mean diameter at breast height (DBH) of 28.5
cm and a mean tree height of 23 m. In 2013, mean tree age was estimated
to be about 110 years. The soil is humic alisol with a variable depth
(40–100 cm), developed on calcareous bedrock. For the period
1989–2014, the mean annual temperature was 7.2°C, and the mean annual
precipitation was 1178 mm, of which 10% concentrated during the summer
(Guidolotti, Rey, D’Andrea, Matteucci & De Angelis 2013; Collaltiet al. 2016; Rezaie et al. 2018; Reyer et al.2020). The experimental area is part of the LTER network (Long Term
Ecological Research) since 1996.
The temperature and precipitation for the period 1989-2015, available on
the Fluxnet2015 release, were used to characterize the, on average,
climate conditions of the site. For the data gaps occurred during the
experimental trial (2016-2017), we used the ERA5 database produced by
the European Centre for Medium-Range Weather Forecasts (ECMWF)
(https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era5,
data accessed: [12/04/2018]), according to the Fluxnet2015 release
formulations (Pastorello et al. 2020). To evaluate peculiarities
of the climatic conditions in 2016 and 2017 we calculated monthly
differences with respect to the average values of precipitation and
temperature observed in the site in the historical time series
1989-2015.
Leaf phenology was monitored using the MODIS Leaf Area Index product
(LAI, MOD15A2H product, https://modis.gsfc.nasa.gov/ ) with 8-days
temporal resolution and 500-meters spatial resolution (Myneni et
al. , 2015). Critical dates, representing approximately linear
transitions from one phenological phase to another, were identified and
defined according to Zhang et al. (2003) as: (1) green-up ,
photosynthetic activity onset; (2) maximum LAI , supposed to be
the leaf maturity phase; (3) senescence , sharp decrease
of photosynthetic activity and green leaf area; (4) winter
dormancy . In 2016, the leafless period after the late frost was
identified from the day of the extreme event and the second green up.
Selection, measurements and sampling of trees
Five trees were selected according to their similarity with site tree
ring chronology, the trees had a DBH ranging from 49 to 53 cm and a mean
age of 109 ± 4 years. Trees were monitored from April 2016 to November
2017. Intra-annual radial growth of each selected tree was measured
using permanent girth bands with 0.1 mm accuracy (D1 Permanent Tree
Girth, UMS, Germany). Furthermore, stem diameter was recorded at the
moment of each sampling of xylem for biochemical analyses (20 sampling
dates from April 2016 to November 2017).
From each tree, micro-cores (2 mm diameter, 15 mm long) of wood were
collected after bark removal, using the Trephor tool (Rossi, Menardi,
Fontanella & Anfodillo 2005). All samples for biochemical analyses were
immediately placed in dry ice for transport to the laboratory, then
stored at −20 °C and, finally, stabilized through lyophilisation
processes until NSCs analysis.
Daily radial increment (Ri, μm
day–1), was calculated as follow:
\(R_{i}=\frac{R_{t}-R_{t-1}}{t}\) eq.1
where R is the radius of each i tree (μm), t is the
date of sampling, and Δt is the time interval between the two
sampling dates expressed in days.
In November 2017, at the end of the experimental trial, increment cores
were collected at breast height from each tree, using an increment
borer. Tree ring width series were converted into tree basal area
increment (BAI, cm2 year–1),
according to the following standard formula:
\(BAI=\pi\ \left(R_{n}^{2}-R_{n-1}^{2}\right)\) eq.2
with n being the year of tree-ring formation.
Starch and soluble sugar concentrations analysis
The freeze-dried xylem samples were milled to a fine powder and used for
all analytical tests. For analysis of glucose, fructose, sucrose and
starch, 10 mg of dry xylem powder were extracted in 1 ml of 80%
ethanol/water at 80 °C for 45 minutes. After centrifugation at 16,000 x
g for 5 minutes, soluble sugars were recovered in the supernatant while
the pellet was resuspended in 1 ml of 40 mM acetate buffer (pH 4.5),
then re-centrifuged 16,000 x g for 5 minutes. This procedure was
repeated 4-times. The final pellet was autoclaved for 45 minutes at 120
°C in the same wash buffer. Enzymatic starch hydrolysis and the
following glucose spectrophotometric assay were done as described by
Moscatello et al. (2017). The supernatant solution containing
soluble sugars was filtered on 0.2 μm nylon filters (GE-Whatman,
Maidstone, UK), then analyzed by high-performance anion exchange
chromatography with pulsed amperometric detection (HPAEC-PAD) (Thermo
Scientific™ Dionex™ ICS-5000, Sunnyvale, CA U.S.A.)(Proietti et
al. 2017).
Modelling of Intra-annual dynamics of non-structural
carbohydrates
To evaluate the effects of the spring late frost (2016) and the heat
wave and drought stress (2017) on the intra-annual NSCs dynamic, a
representative benchmark of the typical intra-annual carbohydrates
dynamic of the study site was needed. With this aim, a dataset on NSCs
dynamic derived from other experimental trials at the site was used
(Supporting Information Table S1). Dataset was composed of data of
different years (i.e.: 2001, 2002, 2013, 2014, 2015, and 2018). This
dataset included 39 observations of starch dynamic and 28 observations
for both soluble sugars (glucose, fructose and sucrose) and total NSCs
dynamic. Observations for soluble sugars were lower, because of the
methodological sampling procedure used in 2015. During that year, woody
samples were collected for xylogenesis analysis and maintained in
ethanol-formalin acetic acid solution (FAA). Unfortunately, this
methodology caused the loss of soluble sugars, while the starch
integrity was preserved, as verified by means of specific analytical
tests on woody tissues.
Different models based on data of starch, soluble sugars and total NSCs
were used looking for possible patterns within the years and tested
through the Akaike Information Criterion (AIC) (Akaike 1974; Aho,
Derryberry & Peterson 2014) to select the simplest model able in
reproducing the in situ observed pattern. The AIC quantifies the
trade-off between parsimony and goodness-of-fit in a simple and
transparent manner, estimating the relative amount of information lost
by a given model. Hence, the model showing the lowest AIC is considered
the model with the smallest information loss and, potentially, the most
representative one (Akaike 1974). The four assumptions of linear model
(homoscedasticity, normality of the error distribution, statistical
independence of the errors and absence of influential points) were
tested graphically (Fig. S1 - 3).
Statistical data analysis
Intra annual differences among contents of starch and total sugars were
tested using one-way repeated measures analysis of variance (one factor
repetition), using sampling date as predictive factor. The measured data
of soluble sugars did not pass the normality test and were analysed by
Repeated Measures Analysis of Variance on Ranks. Multiple comparisons
were performed by the
Student-Newman-Keuls Method.
Linear mixed models, considering “tree” and “sampling date” as
crossed random effects, were used to account for the random variation of
inter-annual starch, soluble and total sugar contents. Statistical
analysis and figures were made using R 3.5.0 (R Development Core Team
2018).
Differences among modelled and measured data were identified using the
interval of confidence (1.96 Standard error, SE), the lack of overlap
between the two intervals of confidence indicates likely a statistically
significant difference at the 95% level (P-value<0.05).